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Heterogeneous impact of climate policy uncertainty on regional economic development in China: Evidence from double machine learning.

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Bibliographic Details
Title: Heterogeneous impact of climate policy uncertainty on regional economic development in China: Evidence from double machine learning.
Authors: Huang Z; School of Business, Shantou University, Shantou, China. Electronic address: zhlhuang@stu.edu.cn., Zhang Q; School of Business, Shantou University, Shantou, China. Electronic address: 22qyzhang@stu.edu.cn., Zheng Y; School of Mathematics and Computing, Shantou University, Shantou, China., Huang F; School of Business, Shantou University, Shantou, China., Guo R; School of Mathematics and Computer Science, Shantou University, Shantou, China.
Source: Journal of environmental management [J Environ Manage] 2025 Oct; Vol. 393, pp. 127046. Date of Electronic Publication: 2025 Aug 26.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Academic Press Country of Publication: England NLM ID: 0401664 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-8630 (Electronic) Linking ISSN: 03014797 NLM ISO Abbreviation: J Environ Manage Subsets: MEDLINE
Imprint Name(s): Original Publication: London ; New York, Academic Press.
MeSH Terms: Economic Development* , Machine Learning* , Climate Change*, China ; Uncertainty
Abstract: Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Climate change presents a significant challenge to the global economy. To achieve the goal of carbon neutrality, China has proposed diverse climate policies. However, the uncertainty surrounding these policies poses substantial risks, especially in regions with significant differences in economic development levels. This study explores the heterogeneous impact of climate policy uncertainty (CPU) on the gross output value per capita in regions with different economic development levels. Firstly, a cluster analysis of provincial economies is conducted, and then a double machine learning (DML) method is employed for causal inference analysis. Finally, the model is verified through a robustness test, further confirming the robustness and accuracy of the conclusions. The results show that the impact of CPU differs considerably among regions with varying economic development levels: a significant positive effect is observed in developed regions, a moderate positive effect in moderately developed regions, a negative effect in less-developed regions, and a slight positive effect in economically underdeveloped regions. In the process of promoting the "dual carbon" goals, formulating differentiated policy responses based on the industrial structure and fiscal situation of different regions is of great significance for mitigating the adverse effects brought about by CPU.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)
Contributed Indexing: Keywords: Climate change; Climate policy uncertainty; Double machine learning; Heterogeneous impact; Regional economic development
Entry Date(s): Date Created: 20250827 Date Completed: 20251015 Latest Revision: 20251015
Update Code: 20251015
DOI: 10.1016/j.jenvman.2025.127046
PMID: 40865316
Database: MEDLINE
Description
Abstract:Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />Climate change presents a significant challenge to the global economy. To achieve the goal of carbon neutrality, China has proposed diverse climate policies. However, the uncertainty surrounding these policies poses substantial risks, especially in regions with significant differences in economic development levels. This study explores the heterogeneous impact of climate policy uncertainty (CPU) on the gross output value per capita in regions with different economic development levels. Firstly, a cluster analysis of provincial economies is conducted, and then a double machine learning (DML) method is employed for causal inference analysis. Finally, the model is verified through a robustness test, further confirming the robustness and accuracy of the conclusions. The results show that the impact of CPU differs considerably among regions with varying economic development levels: a significant positive effect is observed in developed regions, a moderate positive effect in moderately developed regions, a negative effect in less-developed regions, and a slight positive effect in economically underdeveloped regions. In the process of promoting the "dual carbon" goals, formulating differentiated policy responses based on the industrial structure and fiscal situation of different regions is of great significance for mitigating the adverse effects brought about by CPU.<br /> (Copyright © 2025 Elsevier Ltd. All rights reserved.)
ISSN:1095-8630
DOI:10.1016/j.jenvman.2025.127046